Abstract
With the rapid development of energy Internet (EI), energy storage (ES), which is the key technology of EI, has attracted widespread attention. EI is composed of multiple energy networks that provide energy support for each other, so it has a great demand for diverse energy storages (ESs). All of this may result in energy redundancy throughout the whole EI system. Hence, coordinating ESs among various energy networks is of great importance. First of all, we put forward the necessity and principles of energy storage coordination (ESC) in EI. Then, the ESC model is constructed with the aim of economic efficiency (EE) and energy utilization efficiency (EUE) respectively. Finally, a multi-agent particle swarm optimization (MAPSO) algorithm is proposed to solve this problem. The calculation results are compared with that of PSO, and results show that MAPSO has good convergence and computational accuracy. In addition, the simulation results prove that EE plays the most important role when coordinating various ESs in EI, and an ES configuration with the multi-objective optimization of EE and EUE is concluded at last.
Highlights
As the situations of climate change and energy crises become more severe, renewable energies will gradually replace fossil energies, leading to fundamental changes in the structure of the energy system
Suppose the total required energy of a certain energy Internet (EI) system provided by energy storage (ES) is Etotal, and the energy here refers to converted energy, which is different from stored energy, and the following is the same
Though the processing time of multi-agent particle swarm optimization (MAPSO) is longer than particle swarm optimization (PSO), MAPSO is a valid algorithm in terms of computing results
Summary
As the situations of climate change and energy crises become more severe, renewable energies will gradually replace fossil energies, leading to fundamental changes in the structure of the energy system. With the optimization of EE and EUE, Zhu et al [24] applied particle swarm optimization (PSO) to solve the problem of energy storage coordination (ESC) in EI. In the context of EI, this paper optimizes energy storage coordination (ESC) from the perspective of EE and EUE To handle these issues, multi-agent particle swarm optimization (MAPSO) is proposed in this paper. EI involves a variety of energy sources and energy conversion, and the structure and mode of operations are more complex and diverse Both the capacity and type of ESs are in great demand, and diverse functions of EI can be achieved with abundant uses of ESs. according to the overall demand of an EI system, three scenarios with the optimization of EE and EUE (uniting EE and EUE) are demonstrated in this study.
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